Time encoding migrates from prefrontal cortex to dorsal striatum during learning of a self-timed response duration task.

Although time is a fundamental dimension of life, we do not know how brain areas cooperate to keep track and process time intervals. Notably, analyses of neural activity during learning are rare, mainly because timing tasks usually require training over many days. We investigated how the time encoding evolves when animals learn to time a 1.5 s interval. We designed a novel training protocol where rats go from naive- to proficient-level timing performance within a single session, allowing us to investigate neuronal activity from very early learning stages. We used pharmacological experiments and machine-learning algorithms to evaluate the level of time encoding in the medial prefrontal cortex and the dorsal striatum. Our results show a double dissociation between the medial prefrontal cortex and the dorsal striatum during temporal learning, where the former commits to early learning stages while the latter engages as animals become proficient in the task.

Characterizing Speed Cells in the Rat Hippocampus.

Spatial navigation relies on visual landmarks as well as on self-motion information. In familiar environments, both place and grid cells maintain their firing fields in darkness, suggesting that they continuously receive information about locomotion speed required for path integration. Consistently, "speed cells" have been previously identified in the hippocampal formation and characterized in detail in the medial entorhinal cortex. Here we investigated speed-correlated firing in the hippocampus. We show that CA1 has speed cells that are stable across contexts, position in space, and time. Moreover, their speed-correlated firing occurs within theta cycles, independently of theta frequency. Interestingly, a physiological classification of cell types reveals that all CA1 speed cells are inhibitory. In fact, while speed modulates pyramidal cell activity, only the firing rate of interneurons can accurately predict locomotion speed on a sub-second timescale. These findings shed light on network models of navigation.